A Rule Sets Ensemble for Predicting MHC II-Binding Peptides
Computational modeling of predicting which peptides can bind to a specific MHC molecule is necessary for minimizing the number of peptides required to synthesize and advancing the understanding for the immune response. Most prediction methods hardly acquire understandable knowledge and there is still some space for the improvements of prediction accuracy. Thereupon, Rule Sets Ensemble (RSEN) algorithm based on rough set theory, which utilizes expert knowledge of bindingïmotifs and diverse attribute reduction algorithms, is proposed to acquire understandable rules along with the improvements of prediction accuracy. Finally, the RSEN algorithm is applied to predict the peptides that bind to HLA-DR4(B1*0401). Experimentation results show: 1) compared with the individual rule sets, the rule sets ensembles have significant reduction in prediction error rate; 2) in prediction accuracy and understandability, rule sets ensembles are better than the Back-Propagation Neural Networks (BPNN).
KeywordsMajor Histocompatibility Complex Attribute Reducts Major Histocompatibility Complex Molecule Average Error Rate Training Part
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